Publications

(This essay appeared in DM-list as a reply to R. Dybowski Call for Papers
for ML Journal Issue on Fusion of Knowledge with Data, see
item 31. GPS)

Dear Mr. Dybowski:

When I asked the DM list for a definition of data mining, I got
enough interesting answers to scribble out several pages of notes. At the
first level this exercise gave a very good answer to my question. At the
second order of analysis, it tells us something about the problem of
arriving at definitions in general. Definitions are ubiquitous in the
testing of (real) human intelligence. Perhaps this exercise has something
to say about your special issue as well. I assume that those giving the
definitions were conscious of their answers as they typed them or
verbalized them to a voice recognition program. I also assume that these
definitions are not carried about in the conscious mind at all
times. Those answering had to reach into the unconscious and retrieve the
definitions or develop them. Perhaps then we should look more closely at
the history of work in introspective psychology and the study of the
unconscious mind.

I think the short essay below might be suitable as a letter to the
Machine Learning Journal. If so perhaps a title would be "Knowledge
Engineering: What happens when the human unconscious is made conscious and
installed in learning machines?"

The 'call for papers' of this special issue of the Machine Learning
Journal notes that "knowledge engineering and machine learning remain
largely separate disciplines." How can they be "combined to construct
decision support systems?" it asks. I am going to give one answer to that
question by going back to nineteenth century introspectionism and the
quest for a breakthrough in (real) human intelligence. I think this will
provide some fruitful ideas on how we can advance current
(artificial) machine intelligence and learning.

Chapter 13, "Psychology Becomes Self-Conscious" from Brett's "History
of Psychology" (1912; 1921) tells us that "In German psychology in the
nineteenth century two main trends were discernible which are of
methodological interest. Psychologists were very preoccupied with the self
or soul as a possible object of study; they were also very concerned about
themselves and the status and terms of reference of their developing
inquiries." (p. 533). If human subjects could look within themselves and
tell programmers how their intellect works, great advances in AI and
machine learning would be made. This would constitute knowledge
engineering applied to everyday intellectual skill. Isn't this a
reasonable quest for a species which goes by the name of "homo sapiens"?
Further to that, what will the status of "robo sapiens" be when this
undertaking is successful?

The self is largely unconscious as any layman will tell you
today. Brett credits Von Hartmann (1842-1906) with developing the Doctrine
of the Unconscious, particularly with his 1901 book, Die Moderne
Psychologie, qualifying this by saying "Though to some of his
contemporaries Hartmann appeared to be wholly original, two-thirds of the
Doctrine of the Unconscious was already commonplace." (p. 578). Brett's
history speaks highly of Fechner. "The study of Fechner's life is one of
the most instructive ways of following the progress of thought in the
nineteenth century...in the year 1860, the Elemente der Psychophysik was
completed." (pp. 580-581). Given that "His Psychophysik was not the mere
invention of a science; it was the attainment of a new plane of
thought" (p. 584), we need to take it seriously. Though every psychology
student since that era has had to memorize the Weber-Fechner Law, my
interest presently has to do with the general methodology of
introspectionism and not its nineteenth century findings. Most succintly
this methodology is expressed in the following: "The last source of
knowledge is Einfuhlung or self-objectification." (p. 609).

The problems of machine learning and AI today are still largely the
problems of self-objectification and the turning of unconsciousness into
consciousness. For example, how can we develop software to clearly
articulate (a) reading skill; (b) everyday conversational
skill; (c) object recognition skill? All three are everyday skills or
abilities. We take them for granted. Yet they are largely unconscious to
us. To appreciate how remarkable our agnosia is, consider other skills by
contrast. Imagine a bus driver who says he does not know how he drives the
bus...it 'just happens'. Consider your surprise upon hearing your dentist
profess to have little conscious knowledge of how dentistry is done. Isn't
it amazing then that everyday skills like those of reading (with
comprehension), conversing and recognizing objects are mostly of an
unconscious nature when it comes to HOW we do them? Homo sapiens indeed.

The reward for an understanding of "The Psychology of Everyday
Things" (the title of a book by computer scientist/psychologist Donald
Norman) would be be no less than a comprehensive understanding of how
to write software for intelligent machines which will meet or exceed human
equivalency. In this case I mean reading, conversing and the recognizing
of objects as 'everyday things'. Individual Differences, as I note in my
1976 text by that name (with A. Buss; Gardner Press) are ubiquitous. Every
clinical psychologist knows too that some patients are more insightful
than others (eg, see Poley, Lee and Vibe, "Alcoholism: A Treatment
Manual", 1979, Gardner Press). It is therefore my expectation that if a
notice were posted on the world wide web seeking assistance from those who
can consciously articulate that which is unconscious to most people, most
of the time, the effort would produce good results. Whether by natural
talent or training or both, those with exceptional introspective abilities
with respect to "everyday things" are likely to be in the world
population. The solution to the presenting problem of knowledge
engineering and machine learning then is suggested via social
psychology and marketing psychology as much as anything else. To find and
engage the assistance of those who can do knowledge engineering in these
domains would have to be posted by a well known and authoritative body to
be effective, eg ACM (Association of Computing Machinery).

Skinner's dictum "If it can be verbalized, it can be programmed" is
true or mostly true. I have yet to see a definitive answer as to whether
he meant this to include both the broad and narrow (computing
science) definition of "programmed" but I will assume that the answer is
yes. After all, the Mark I computer was built at Harvard in the 1940's and
Skinner must have understood what computing science meant by that
word. Lets find those gifted people who can verbalize for us how they read
with comprehension, converse and recognize objects. When they give us the
benefit of their knowledge engineering in the domain of everyday
intellectual skills, we will have some valuable software. The software
will be verbalized or written by them in EL, "Everyday Language". Since a
standard computer language like LISP is just chicken scratchings without
meaning, I think we can call a script of meanings in EL, "software" as
much as the LISP or Fortran or COBOL into which it is transcribed. Thus we
would be looking for software writers in the world population who have
never thought of themselves in these terms. Some marketing advantages
accompany this.

Once this is successful, there will be a "fusion" of domain knowledge
(in the domains of reading, conversing and recognizing objects) with the
data to which the domain knowledge is applied. Add that capability to
existing capabilities in memory and logic/arithmetic/mathematical ability
and you have powerful software for "decision support". It will be so
powerful that it should meet or exceed the Moravec criterion of human
equivalency for the achieved products of factors of primary mental ability
in humans (see Ch.3 of "Individual Differences"). In other words we will
have a machine with "superhuman AI". Even if the introspectionists in this
project are limited, they will achieve partial results and those partial
results may enable less gifted people to discover some rules for further
knowledge engineering.

In closing, let's describe the successful outcome of such a project in
terms which are as dramatic as they are realistic upon successful
completion. Imagine humanoids like the recent Honda and Sony
prototypes programmed with such capabilities. When I coined the expression
"robo sapiens" I had in mind the prospect of human equivalency in
intellectual ability. But a human look-alike dramatizes the concept,
especially if the humanoids can walk among humans and are given at least a
degree of autonomy. Imagine ASIMO being able to carry on an everyday
conversation as well as a typical human, answer questions about any text
as well as a human and able to recognize objects as well as a human. In my
opinion, gifted introspectionist humans would enable us to make
significant progress in that direction. And few doubt that robo sapiens
will attain human equivalency some time in this century. Thus the prospect
of trying to attain it with a mega-project planned now for completion over
the next decade and with a budget comparable to that of the International
Space Station should not be ruled out. When homo sapiens becomes fully
conscious and programs robo sapiens with that consciousness, what will the
theologians and philosophers say? Is there any reason robo sapiens,
particularly after a few generations of learning and self-improvement will
not confound us with the answers? What will we say when robo sapiens tells
us it is conscious and gives better lectures and answers on what it means
to be conscious than any human?